


*       ********************************************************************* *
*       Replication Code for Online Appendix for "`Because He's Gay': How Race, Gender, and Sexuality   
*       Shape Perceptions of Judicial Fairness"
*       Journal of Politics
*       Authors: Ana Bracic, Mackenzie Israel-Trummel, Tyler Johnson, and Kathleen Tipler
*       ********************************************************************* *
 
 
*       ********************************************************************* *
*       Analyses of Open Ended Statements Presented in Sections 4.4 and 4.5   *
*       ********************************************************************* *


*		********************************************************************* *
*		Analyses of statements are in the order they appear in the main text  *
*		********************************************************************* *


*       ***************************************** *
*	 	SECTION 4.4: Are the Choices Deliberate?  *	 
*       ***************************************** *

use conjoint_judges_open_ended.dta


*To address the possibility of respondents complaining about the conjoint being a false choice, we looked at responses coded 88 on variable riddle and then we counted, by hand, the number of responses that 1) commented on having to choose a judge and 2) commented that it was a coin toss. 


#delimit;

foreach var of varlist race gender sexuality party age lawschool previousjob generalminority refertolegalcase racist sexist homophobic ageist antiparty antischool antijob ideologyopenliberal ideologyclosedmorals judgesameasme moreexperience impartial knowsdiscrimination mydesiredoutcome doesntfit riddle {;
	tab `var';
	};

#delimit cr	


*From these tabulations we see that the most common attributes mentioned were prior occupation (21.6%), age (13.4%), sexuality (11.9%), and gender (11.1%). About 7.6% of responses were nonsensical and 11.6% did not fit any of our coding categories. We also see that 5.7% of respondents chose a judge because they would be familiar with discrimination. Combining counts for ideologyopenliberal and ideologyclosedmorals we see that about 10% of respondents invoked political motivation in their choice.

 
*generating a count to see how many multiple categories there are and how many overall blank responses we have

gen total_reasons =.
replace total_reasons = race + gender + sexuality + party + age + lawschool + previousjob + generalminority + refertolegalcase + racist + sexist + homophobic + ageist + antiparty + antischool + antijob + ideologyopenliberal +  ideologyclosedmorals+  judgesameasme+  moreexperience + impartial + knowsdiscrimination +  mydesiredoutcome + doesntfit

tab total_reasons
 
*Of the 4,331 decisions, 7.5% provided no justification. 
 
 
*generating a count of statements that mention an identity
  
gen total_identity_reasons =.
replace total_identity_reasons = race + gender + sexuality + generalminority 

tab total_identity_reasons

*In 26.7% of answers respondents mentioned a judge’s race, gender, sexuality, or general minority status


*generating a count of intolerant statements

gen total_animus_reasons =.
replace total_animus_reasons = racist + sexist + homophobic + ageist

tab total_animus_reasons

*3.9% of statements were explicitly intolerant



*       ***************************************** *
*		SECTION 4.5: Does Case Type Matter ?  	  *
*       ***************************************** *


*are people more likely to bring up race in the race case and so on?

*generating dummies for each case type

gen race_case =.
replace race_case =1 if discrimtype =="racial"
replace race_case =0 if discrimtype !="racial"
replace race_case =. if pipe1 == ""


gen gender_case =.
replace gender_case =1 if discrimtype =="gender"
replace gender_case =0 if discrimtype !="gender"
replace gender_case =. if pipe1 == ""

gen gender_id_case =.
replace gender_id_case =1 if discrimtype =="gender identity"
replace gender_id_case =0 if discrimtype !="gender identity"
replace gender_id_case =. if pipe1 == ""

gen sexuality_case =.
replace sexuality_case =1 if discrimtype =="sexual orientation"
replace sexuality_case =0 if discrimtype !="sexual orientation"
replace sexuality_case =. if pipe1 == ""


gen religion_case =.
replace religion_case =1 if discrimtype =="religious"
replace religion_case =0 if discrimtype !="religious"
replace religion_case =. if pipe1 == ""


*doing an independent groups t-test comparing each case to just the religion case; 
*creating a variable where religion is default (0) and other case is 1;

gen religion_race_cases =.
replace religion_race_cases = 0 if religion_case ==1
replace religion_race_cases = 1 if race_case ==1
label var religion_race_cases "0 if religion case, 1 if race case"


gen religion_gender_cases =.
replace religion_gender_cases = 0 if religion_case ==1
replace religion_gender_cases = 1 if gender_case ==1
label var religion_gender_cases "0 if religion case, 1 if gender case"


gen religion_gender_id_cases =.
replace religion_gender_id_cases = 0 if religion_case ==1
replace religion_gender_id_cases = 1 if gender_id_case ==1
label var religion_gender_id_cases "0 if religion case, 1 if gender identity case"


gen religion_sexuality_cases =.
replace religion_sexuality_cases = 0 if religion_case ==1
replace religion_sexuality_cases = 1 if sexuality_case ==1
label var religion_sexuality_cases "0 if religion case, 1 if sexuality case"



ttest gender, by(religion_gender_cases)
*Footnote 11: 16.5% mention gender in the sex discrimination case compared to 6.8% in the religion case (p < 0.05).

ttest gender, by(religion_gender_id_cases)
*Footnote 12: 10% mention gender in the gender identity case compared to 6.8% in the religion case (p < 0.05). 

ttest sexuality, by(religion_gender_id_cases)
*Footnote 13: 18.5% mention sexuality in the gender identity case compared to 7% in the religion case (p < 0.05).  

ttest race, by(religion_race_cases)
*Footnote 14: 18.1% mention race in the racial discrimination case compared to 5.8% in the religion case (p < 0.05).

ttest sexuality, by(religion_sexuality_cases)
*Footnote 15: 17.5% mention sexuality in the sexual orientation case compared to 7% in the religion case (p < 0.05).


ttest previousjob, by(religion_case)

*how many times does prior occupation appear in the religion case compared to other cases? Footnote 16: 27.1% mention previous occupation in the religion case compared to 20.3% in all other cases (p < 0.05).



